Search
15 articles for “AI Enabled Drug Discovery”
-
AI Powered Invention in Pharmaceuticals Boosting Innovation
Abstract: Artificial intelligence has the potential to transform the drug discovery process, making the process more efficient, accurate and faster. But the success of artificial intelligence depends on the availability of good data, resolution of ethical issues, and awareness of the limitations of artificial intelligencebased methods. The present article examined the benefits, challenges, and shortcomings of skills in the workplace and suggested strategies and practical actions to overcome current challenges. Data …
Published in Research and Reviews: A Journal of Pharmaceutical Science · Vol. 15, Issue 2, 2024 · pp. 102–107 Read article
-
A Review on Anesthetic Drug Discovery with Computer Aided Drug Design
Abstract: In contemporary medicine, anaesthesia is essential for enabling surgical procedures, pain control, and patient comfort. Finding and creating safe and efficient anaesthetics is crucial to enhancing patient outcomes and developing the medical field. In this review, we provide a comprehensive overview of anesthetic drug discovery with a focus on the integration of computer-aided drug design (CADD) methodologies. Beginning with an exploration of anesthesia mechanisms and targets, we delve into the …
Published in International Journal of Antibiotics Read article
-
Analysis of Bioinformatics Software Applied in Computer-Aided Drug Design
Abstract: The integration of bioinformatic tools with computational methods has revolutionized the field of Computer-aided Drug Design (CADD), enabling researchers to expedite the discovery and optimization of new therapeutics. This review provides an in-depth analysis of the bioinformatic tools utilized in CADD, encompassing molecular docking, molecular dynamics simulation, virtual screening, homology modelling, and molecular visualization. We go over the tenets, approaches, and uses of these instruments, emphasizing their value in expediting …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 11, Issue 2, 2024 · pp. 1–10 Read article
-
Innovations in Targeted Drug Discovery for Personalized Medicine
Abstract: Personalized medicine is revolutionizing modern healthcare by custoizing treatment plans to match an individual’s genetic makeup, protein expression, and metabomlic characteristics. Also referred to as precision medicine, this approach seeks to improve therapeutic outcomes, reduce adverse effects, and make efficient use of healthcare resources. The incorporation of various omics technologies—including genomics, proteomics, transcriptomics, metabolomics, and epigenomics—has greatly advanced our ability to understand disease biology and molecular variations specific to each …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 12, Issue 3, 2025 · pp. 19–41 Read article
-
AI-based Drug Discovery-Revolutionizing Pharmaceutical Research
Abstract: The traditional drug discovery process is often costly, time-consuming, and prone to high failure rates. The advent of Artificial Intelligence (AI) has revolutionized this field by significantly enhancing efficiency, reducing costs, and improving success rates. AI-driven approaches, including machine learning (ML), deep learning (DL), and natural language processing (NLP), have transformed key areas such as drug target identification, molecular screening, lead optimization, and clinical trial design. AI models can analyze …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 12, Issue 2, 2025 · pp. 30–44 Read article
-
Computer Aided Drug Designing for Targeted Drug Delivery Systems
Abstract: The discovery and development of a contemporary sedate is widely acknowledged as an extremely difficult task that requires significant resources and effort. Therefore, computer-aided medication design techniques are widely utilized today to increase the productivity of the drug discovery and development process. Among structure-based and ligand-based drug design approaches, both recognized for their efficiency in drug discovery and development, various CADD (Computer-Aided Drug Design) techniques are evaluated based on specific …
Published in Trends in Drug Delivery · Vol. 11, Issue 2, 2024 · pp. 34–41 Read article
-
AI Application in the Creation of Medications for COPD
Abstract: The crippling lung condition known as chronic obstructive pulmonary disease (COPD) is typified by a continuous restriction of airflow, which results in increased respiratory dysfunction and a reduced quality of life. The rising incidence of COPD worldwide emphasizes the pressing need for innovative pharmaceutical approaches to address the illness. Even though COPD care has advanced significantly, most current medications concentrate on symptom relief rather than disease change. This gap in …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 12, Issue 2, 2025 · pp. 01–05 Read article
-
Emerging Digital Trends in Virology Software: Optimizing Viral Discovery, Surveillance,and Patient Management.
Abstract: Virology and antiviral therapeutics are being reshaped by rapid advances in computational tools, automation platforms, and virus-focused digital health applications. Software systems now span the entire virology value chain, from in silico viral target identification and antigen design, to AI-supported clinical trial management for vaccines and antivirals, to post-marketing pharmacovigilance and patient-facing mobile tools. This review examines current and emerging software trends relevant to virus studies, emphasizing applications in viral …
Published in International Journal of Virus Studies · Vol. 3, Issue 1, 2026 · pp. 29–38 Read article
-
Artificial Intelligence in Microbiological Research: Methods, Applications and Implications
Abstract: Artificial Intelligence (AI) is revolutionising microbiological research by enabling the rapid analysis of complex biological data and improving the accuracy, efficiency, and reliability of scientific investigations. Recent advances in machine learning, deep learning, and bioinformatics have transformed AI into a powerful tool for studying microorganisms, their genetic composition, evolutionary patterns, and interactions with hosts and the environment. AI-driven computational models can process large and complex datasets far more efficiently than …
Published in Research and Reviews: A Journal of Microbiology and Virology · Vol. 16, Issue 2, 2026 Read article
-
Artificial Intelligence in Drug Repurposing: A Short Impact Assessment
Abstract: Artificial intelligence (AI) in pharmaceutical repurposing has become a game-changing tool that opens new avenues for the application of new drugs that have already been approved. Traditional drug discovery is a lengthy and expensive process, whereas AI can rapidly analyze vast datasets of biological, chemical, and clinical information to predict drug-disease interactions. AI-driven techniques, such as machine learning, natural language processing, and deep learning, enable the identification of potential repurposing …
Published in Trends in Drug Delivery · Vol. 11, Issue 3, 2024 · pp. 42–45 Read article
-
Computational Simulations in Drug Discovery: Modeling Protein Folding and Drug Binding
Abstract: Computational simulations have become essential tools in drug discovery, offering unprecedented insights into molecular behavior at the atomic level. These simulations, particularly in the domains of protein folding and drug binding, allow for the exploration of complex biological systems that are often difficult to study experimentally. Protein folding, a critical aspect of drug discovery, involves the transition of a polypeptide chain from an unfolded to a biologically active structure. Understanding …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 1, 2025 · pp. 23–29 Read article
-
QSAR Modeling Techniques: A Comprehensive Review of Tools and Best Practices
Abstract: Quantitative Structure–Activity Relationship (QSAR) modeling has become an essential tool in drug discovery, toxicity assessment, and environmental chemistry. By correlating chemical structure with biological activity or toxicity, QSAR enables the prediction of compound behavior without extensive experimental testing. This approach not only saves time and resources but also supports ethical practices by reducing reliance on animal studies. The evolution of QSAR from basic linear models to advanced machine learning and …
Published in International Journal of Cheminformatics · Vol. 3, Issue 1, 2025 · pp. 56–63 Read article
-
Progression of Health and Wellness: Artificial Intelligence (AI) and Deep Learning (DL) for Precision Medicines
Abstract: Deep learning and artificial intelligence in the field of precision medicine is revolutionizing healthcare to make personalized therapeutic approaches desirable based on the unique characteristics of the patient. AI technologies improve diagnostic accuracy by analyzing medical data, spotting patterns and anomalies that human experts may miss. AI-driven models are instrumental in precision medicine, where they can predict patient response to therapies to tailor treatment plans, enhancing outcomes and reducing adverse …
Published in Emerging Trends in Personalized Medicines · Vol. 2, Issue 2, 2025 · pp. 1–5 Read article
-
Drug Design and Process Chemistry: Bridging the Gap between Discovery and Manufacturing
Abstract: This review explores how drug discovery connects with process chemistry to enable large- scale pharmaceutical manufacturing. While drug discovery identifies and optimizes therapeutic compounds, process chemistry adapts these compounds for mass production, addressing challenges in scalability, cost, and quality. The article examines the role of early collaboration between drug designers and process chemists, recent innovations in green chemistry and flow chemistry, and case studies that illustrate successful scale-up strategies. Emerging …
Published in Trends in Drug Delivery · Vol. 12, Issue 1, 2025 · pp. 09–21 Read article
-
Big Data in Chemistry: Problems and Answers
Abstract: The rapid growth of experimental and computational chemistry data, researchers now have access to vast datasets, presenting both significant opportunities and challenges. This paper explores the primary challenges associated with managing, processing, and utilizing big data in chemistry, including data heterogeneity, integration across various scales and systems, lack of standardized formats, and the need for advanced tools for data analysis. Additionally, the paper discusses the ethical concerns of data ownership, …
Published in International Journal of Cheminformatics · Vol. 2, Issue 1, 2024 · pp. 9–14 Read article